Fluorescence molecular tomography(FMT)is a fast-developing optical imaging modalitythat has great potential in early diagnosis of disease and drugs development.However,recon-struction algorithms have to address a highly ill-posed problem to fulfll 3D reconstruction inFMT.In this contribution,we propose an efficient iterative algorithm to solve the large-scalereconstruction problem,in which the sparsity of fluorescent targets is taken as useful a prioriinformation in designing the reconstruction algorithm.In the implementation,a fast sparseapproximation scheme combined with a stage-wise learning strategy enable the algorithm to dealwith the ill-posed inverse problem at reduced computational costs.We validate the proposed fastiterative method with numerical simulation on a digital mouse model.Experimental results demonstrate that our method is robust for different finite element meshes and different Poissonnoise levels.
结肠管腔中心线的提取在结肠疾病的计算机辅助检测中起到重要的作用,对中心线提取结果的定量评价一直是辅助检测中的难点。从中心线参数方程和截面方程两方面考虑,生成了20套仿真数据,模拟了结肠高曲率、褶皱和管腔的形态特点,提出了一系列评价准则。还对最小路径(DIJ)和最大生成树(MST)算法及作者提出的基于生成树的中心线快速提取算法(Fast centerline extraction algorithm based on Maximal Spanning Tree,简称FMST)进行了性能比较。实验结果验证了最大生成树算法解决了中心线在高曲率的拐角问题,保持中心线提取准确率同时,提取速度平均提高80%以上,是三种算法中性能最好的。
